Trial reportBMC infectious diseases2022
Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study.
Trial report in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.BMC oral health · 2024Pooled it
- Interpretable Machine Learning Model for Fungal Infection Prediction: A Real-World Study.Health care science · 2026Article
- Machine learning-based prediction of ICU admission and mortality in Crimean-Congo hemorrhagic fever by using wide-range targeted metabolomics.BMC infectious diseases · 2026Article
- Development and validation of machine learning models integrating spectral computed tomography-derived three-dimensional quantitative parameters for predicting histopathological grade in pancreatic ductal adenocarcinoma.Journal of gastrointestinal oncology · 2026Article
- Machine learning triumphs in differentiating fungal and bacterial infections.BMC infectious diseases · 2026Article
- IHIT-BED: an interpretable transformer approach using unbiased hematology analyzer impedance data for early identification of bacteremia in emergency department.Bioinformatics advances · 2026Article
- Development and validation of a modified SOFA score for mortality prediction in candidemia patients.Scientific reports · 2025Article
- The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules.BMC cancer · 2025Article
- A Complete Transfer Learning-Based Pipeline for Discriminating Between Select Pathogenic Yeasts from Microscopy Photographs.Pathogens (Basel, Switzerland) · 2025Article
- Prediction of bloodstream infection using machine learning based primarily on biochemical data.Scientific reports · 2025Article
- Artificial intelligence in hospital infection prevention: an integrative review.Frontiers in public health · 2025Review
- Article
- Risk and Prognostic Factors for Bloodstream Infections Due to Clonally TransmittedInfection and drug resistance · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Epidemiological shifts and advances in research on early diagnosis of invasive fungal infection in critically ill patients.Frontiers in cellular and infection microbiology · 2025Review
- Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.Biomedicines · 2024Review
- Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Global Epidemiology of Invasive Infections by UncommonJournal of fungi (Basel, Switzerland) · 2024Review
- Prediction of candidemia with machine learning techniques: state of the art.Future microbiology · 2024Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundInvasive candidal infection combined with bacterial bloodstream infection is one of the common nosocomial infections that is also the main cause of morbidity and mortality. The incidence of invasive Candidal infection with bacterial bloodstream infection is increasing year by year worldwide, but data on China is still limited.
methodsWe included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 2013 to January 2018; we collected and analysed the relevant epidemiological information and used machine learning methods to find prognostic factors related to death (training set and test set were randomly allocated at a ratio of 7:3).
resultsOf the 246 patients with invasive candidal infection complicated with a bacterial bloodstream infection, the median age was 63 years (53.25-74), of which 159 (64.6%) were male, 109 (44.3%) were elderly patients (> 65 years), 238 (96.7%) were hospitalised for more than 10 days, 168 (68.3%) were admitted to ICU during hospitalisation, and most patients had records of multiple admissions within 2 years (167/246, 67.9%). The most common blood index was hypoproteinemia (169/246, 68.7%), and the most common inducement was urinary catheter use (210/246, 85.4%). Moreover, the most frequently infected fungi and bacteria were Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis by machine learning method are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, C-Reactive protein (CRP), leukocyte count, neutrophil count, Procalcitonin (PCT), and total bilirubin level.
conclusionOur results showed that the most common candida and bacteria infections were caused by Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, CRP, leukocyte count, neutrophil count, PCT and total bilirubin level.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.